Device and method for responding to influences of mind

ABSTRACT

In the field of direct mind-machine interactions, prior art devices and methods do not provide sufficiently fast and reliable results. Mental influence detectors ( 100, 140, 400, 430 ) and corresponding methods provide fast and reliable results useful for detecting an influence of mind and hidden or classically non-inferable information. An anomalous effect detector ( 100 ) includes a source ( 104 ) of non-deterministic random numbers ( 110 ), a converter ( 114 ) to convert a property of numbers, a processor to accept converter output ( 118 ) and to produce an output signal ( 124 ) representative of an influence of mind. The processor output signal ( 124 ) contains fewer numbers than the input ( 110 ). A quantum computer ( 400 ) includes a physical source of entropy ( 404 ) to generate output numbers ( 405 ); a source ( 406 ) of test numbers ( 407 ); a measurement processor  410 ) to accept output numbers ( 405 ) and to measure a relationship between process numbers and at least one test number to produce an output ( 414 ) representative of an influence of mind.

TECHNICAL FIELD

The present invention relates to the field of information detection andtransfer, and more specifically to a device and method for responding toinfluences of mind.

BACKGROUND ART

Devices for detecting direct mind-machine interaction (DMMI) have beenproposed and researched for many years. The most carefully controlledand best-explored experiments utilize some type of true, ornon-deterministic, random number generator (TRNG) that produces asequence of random numbers, usually in a binary form. The most commonrandom number generators used are of the electronic type that produce asequence of random binary bits, or events. This has been referred to asa random event generator (REG).

U.S. Pat. No. 6,369,727, issued Apr. 9, 2002, to Vincze, teaches arandom number generator using an analog-to-digital converter to convertrandom noise into digital samples that are transformed by a reductivemapping into uniformly distributed random numbers for output. U.S. Pat.No. 6,581,078, issued Jun. 17, 2003, to Liardet, teaches a random numbergenerator in which a physical voice source produces digital signals thatare combined with signals produced by a pseudo-random number generator.U.S. Pat. No. 4,853,884, issued Aug. 1, 1989, to Brown et al, teaches arandom number generator in which a zener diode produces a random binarynumber output having a controlled degree of randomness determined inresponse to an input control signal.

In typical DMMI experiments, an REG is operated in conjunction with ahuman operator who attempts to influence the statistical properties ofthe REG's output sequence. The operator, or subject, is directed tointend mentally the number of ones produced in the random sequence to beeither higher, lower, or equal to the statistically expected number.

The results of these experiments, compiled over thousands ofexperimental trials, show a small but persistent and statisticallysignificant effect. A most notable example of a research program fordetecting DMMI is the long-standing program at Princeton University,known as Princeton Engineering Anomalies Research (PEAR). This work isdescribed in detail in the book Margins of Reality, the Role of Mind inthe Physical World, by Robert Jahn and Brenda Dunne, Harcourt Brace andCompany, 1987.

The PEAR lab and numerous other facilities around the world haveestablished, to a very high level of statistical significance, theexistence of a link between the mental intention of an operator andresults of measurements of REG output. Demonstrating the reality of DMMIis of great scientific interest. However, the laboratory demonstrationhas not translated into useful devices or methods. Practicalapplications of DMMI have not previously been achieved due to an absenceof understanding of why or how the effect manifests, and because theexperimental devices and data processing methods used are not sensitiveenough to the effect.

Journal articles by many authors have suggested a variety of potentialuses of DMMI. These suggestions are made without disclosing means fortheir implementation. Apparatuses for experiments involving DMMI havebeen complex and expensive. U.S. Pat. No. 5,830,064, issued Nov. 3,1998, to Bradish et al, teaches a method and apparatus of generatingvalues and detecting whether the values fall outside chanceexpectations. This patent involves converting some of the valuesaccording to a selection pattern in order to measure a collectivestatistical variance.

There are, in fact, no practical devices currently on the market thatutilize DMMI. This is because all previous devices and methods haverequired intense and extended effort to produce even a single correctbit of information.

DISCLOSURE OF INVENTION

The invention alleviates some of the limitations mentioned above byproviding devices, systems and methods for detecting and responding toan influence of mind by generating data that is correlated with intendedor desired information, including hidden or non-inferable information,at high rates of speed and high accuracy.

A first basic embodiment of a mental influence detector in accordancewith the invention for responding to an influence of mind comprises ananomalous effect detector. A basic embodiment of an anomalous effectdetector comprises: a source of non-deterministic random numbers; aconverter operable to accept an input of non-deterministic randomnumbers from the source and to convert a property of thenon-deterministic random numbers into a converter output; a processorfor accepting the converter output and producing a processor outputsignal representative of the influence of mind; wherein the processoroutput signal contains fewer numbers than the input of non-deterministicrandom numbers; and an interface that is operable to communicate resultsfrom the processor.

A basic embodiment of a method of detecting an anomalous effectresulting from an influence of mind comprises: providing an input ofnon-deterministic random numbers; converting a property of the input ofnon-deterministic random numbers into a converter output; accepting theconverter output into a processor; and producing in the processor aprocessor output signal representative of the influence of mind, whereinthe processor output signal contains fewer numbers than the input ofnon-deterministic random numbers; and communicating results from theprocessor using an interface. Other embodiments of methods in accordancewith the invention are clear from the descriptions herein. For example,some embodiments of a method of detecting an anomalous effect furthercomprise: providing at least one test number to the processor; measuringa relationship between the converter output in the processor and atleast one test number to produce a relationship measurement; and in theprocessor, abstracting the relationship measurement to provide anenhanced output signal representative of the influence of mind.Abstracting comprises reducing the number of numbers.

A second basic embodiment of a mental influence detector in accordancewith the invention for responding to an influence of mind comprises aquantum computer. A basic embodiment of a quantum computer forresponding to an influence of mind, comprises: a physical source ofentropy operable to generate output numbers; a source of test numbers; ameasurement processor, the processor being operable to accept the outputnumbers, and being further operable to measure a relationship betweenprocess numbers in the processor and at least one test number to producean output representative of the influence of mind, wherein themeasurement processor comprises a relationship processor that isoperable to measure a relationship between process numbers inmeasurement processor and at least one test number; and an interfacethat is operable to communicate results from the processor. A physicalsource of entropy in accordance with the invention embodies quantummechanical properties of superposition and entanglement.

A basic embodiment of a method of using a quantum computer that isresponsive to an influence of mind comprises: generating output numbersusing a physical source of entropy; providing at least one test number;accepting the output numbers in a measurement processor; measuring arelationship between process numbers in the measurement processor and atleast one test number to produce an output representative of aninfluence of mind; and communicating results from the measurementprocessor using an interface.

Accordingly, objects and advantages of the present invention are: toprovide mental influence detectors and methods of detecting a mentalinfluence to obtain useful information in an acceptable time period; toprovide a mental influence detector device that is readily available ata low cost by making it accessible to individual users via the internetand by utilizing components available in the standard configuration ofmost personal computers; to provide a mental influence detector devicethat is hundreds of times more sensitive than previous devices; to bringembodiments of mental influence detectors and methods of using them intopractical and common usage through greatly increased speed andreliability. Further objects and advantages are to provide mentalinfluence detectors and methods that are widely available forexperimentation and demonstration of influences of mind, therebyenhancing understanding and accelerating development of this valuableand untapped technology.

Other features, characteristics and advantages of embodiments inaccordance with the invention will become apparent from consideration ofthe description and drawings below.

BRIEF DESCRIPTION OF DRAWINGS

A more complete understanding of the invention may be obtained byreference to the drawings, in which:

FIG. 1 contains a block diagram representing a general embodiment of ananomalous effect detector in accordance with the invention;

FIG. 2 contains a block diagram representing a preferred generalembodiment of an anomalous effect detector in accordance with theinvention;

FIG. 3 contains a process flow sheet of an exemplary method of operationof an anomalous effect detector in accordance with the invention;

FIG. 4 depicts a block diagram of a system that comprises an anomalouseffect detector in accordance with the invention and that is operable topractice a method in accordance with the invention of detecting aninfluence of mind;

FIG. 5 depicts a block diagram of a quantum computer in accordance withthe invention for responding to an influence of mind;

FIG. 6 depicts a block diagram of a preferred quantum computer inaccordance with the invention for responding to an influence of mind;

FIG. 7 contains a block diagram illustrating a general embodiment of amulti-stream random number generator (RNG) in accordance with thepresent invention;

FIG. 8 contains a block diagram of a self-seeding randomness correctorsuitable for use with a random number generator in accordance with theinvention;

FIG. 9 depicts schematically an XOR gate suitable for use in accordancewith the invention;

FIG. 10 depicts schematically a parity generator suitable for use inaccordance with the invention;

FIG. 11 contains a block diagram of another self-seeding randomnesscorrector suitable for use with a random number generator in accordancewith the invention;

FIG. 12 contains a block diagram of another self-seeding randomnesscorrector suitable for use with a random number generator in accordancewith the invention;

FIG. 13 shows a simplified diagram of a summed multisource random numbergenerator in accordance with the invention having a plurality ofindependent noise sources of random noise coupled at a summing point;

FIG. 14 depicts schematically a simple ring oscillator random numbergenerator (RNG) in accordance with the invention;

FIG. 15 shows an enhanced ring oscillator RNG in accordance with theinvention;

FIG. 16 depicts schematically a random number generator system inaccordance with the invention comprising a plurality of independent ringoscillator RNGs;

FIG. 17 contains a block diagram of a preferred truth table circuit thatis operable to receive an input comprising two sequential random binarybits to produce an output obeying a bias-amplifier truth table;

FIG. 18 contains a block diagram of a preferred truth table circuit thatis operable to receive an input comprising two simultaneous(synchronous) random binary bits to produce an output obeying a truthtable for performing a mutual bias conversion;

FIG. 19 contains a logarithmic graph in which the fraction of runs in asequence of bits of a given type (“1” and “0”) is plotted as a functionof length of runs (in bits);

FIG. 20 contains a block diagram of a circuit having a single storagelatch and operable to convert first order autocorrelation to bias;

FIG. 21 contains a block diagram representing a converter in accordancewith the invention that is operable to accept subsequences of randombinary bits from six sequences of randomness corrected data of a randomnumber source;

FIG. 22 depicts schematically quantum computer in accordance with theinvention comprising a physical source of entropy that is operable togenerate a plurality of output numbers;

FIG. 23 depicts schematically a quantum computer in accordance with theinvention comprising a plurality of entropy sources that are operable togenerate a plurality of output streams; and

FIG. 24 contains a block diagram of an internet-based system comprisingat least one mental influence detector in accordance with the invention.

MODES FOR CARRYING OUT THE INVENTION

The invention is described herein with reference to FIGS. 1-24. Itshould be understood that FIGS. 1-18, 20-24, depicting elements, systemsand processes of embodiments in accordance with the invention, are notmeant to be actual views or diagrams of any particular portion of anactual equipment component, apparatus or process. The figures insteadshow idealized representations that are employed to explain more clearlyand fully the structures, systems and methods of the invention thanwould otherwise be possible. Also, the figures represent only one ofinnumerable variations of structures and systems that could be made oradapted to use a method of the invention. Devices and methods aredescribed with numerous specific details, such as components, oscillatorfrequencies and mathematical techniques, in order to provide a thoroughunderstanding of the present invention. It will be obvious to oneskilled in the art that these specific details are not required topractice the present invention. It is clear that embodiments inaccordance with the invention can be practiced using structures, devicesand processes very different from those of FIGS. 1-18, 20-24. Thepreferred embodiments described herein are exemplary and are notintended to limit the scope of the invention, which is defined in theclaims below.

For the sake of clarity, in some of the figures below, the samereference numeral is used to designate structures and components thatare the same or are similar in the various embodiments described.

The terms “non-deterministic”, “non-deterministic bits”, “true randomnumber”, “true random bits” and related terms are used in thisspecification interchangeably to designate a quality of true randomnessof a number or bit of information, which means that the number or bitcannot be calculated or determined with certainty in advance.Non-deterministic random numbers can be thought to be arbitrary,unknowable, and unpredictable. For the sake of brevity, the abbreviatedterms “random number” and “random numbers” are sometimes used in thisspecification synonymously with the terms denoting non-deterministicnumbers, such as “non-deterministic random number” and “true randomnumbers”. In this specification, the term “entropy” generally refers toa measure of the disorder or randomness of a system or object bearinginformation. A sequence of true random bits uninfluenced by mind has anentropy approaching 1.0 bit of entropy per bit. Embodiments inaccordance with the invention are described herein usually withreference to digital numbers, for example, binary bits. It isunderstood, however, that some embodiments in accordance with theinvention also include the generation and processing of analog numbersinstead of or in addition to the generation and processing of digitalnumbers. The singular and plural forms of the word “number” are usedbroadly and sometimes used interchangeably in this specification. Forexample, the term “non-deterministic random numbers” may indicate ananalog signal in some embodiments, as well as a sequence or subsequenceof binary bits or other digital numbers in other embodiments.

The term “pseudorandom” and related terms in this specification meansdeterministic or algorithmically generated. It is known that somenumbers are able to pass some or all known mathematical tests forrandomness, but still be deterministic, that is, calculable or knowablein advance.

The term “quasi-random” and related terms in this specification refersto a number that includes both true random (i.e., non-deterministic)components and algorithmically generated (i.e., deterministic)components.

The term “mind” in this specification is used in a broad sense. The term“mind” includes a commonly accepted meaning of human consciousness thatoriginates in the brain and is manifested especially in thought,perception, emotion, will, memory, and imagination. The term “mind”further includes the collective conscious and unconscious processes in asentient organism that direct and influence mental and physicalbehavior. Embodiments in accordance with the invention are describedherein usually with reference to a human operator and a human mind. Itis understood, however, that embodiments in accordance with theinvention are also operable to respond to an influence of the minds ofother sentient organisms in addition to humans. Also, embodiments inaccordance with the invention are described herein usually withreference to a conscious human mind in a state of awareness. It isunderstood, however, that embodiments in accordance with the inventionare operable to respond to an influence of a mind not in a state ofconscious awareness. Although the mind of a sentient organism generallyis associated with functions of the brain, the term “mind” in thisspecification is not necessarily limited to functions of the brain, noris the term “mind” in this specification necessarily related tofunctions of the brain.

The term “anomalous effect” and related terms in this specificationinclude influences of mind that are not mediated by classical energiesor forces. In one sense, the terms refer to the effects of mind onnumber sources and on physically measurable properties. Traditionally,concepts associated with anomalous effects have been used to explainsuch phenomena as ESP, Psi, Psychic Phenomena, Remote Viewing,Telepathy, Clairvoyance, Clairaudience, Psychokinesis, Precognition,Mental Powers, among others.

The terms “quantum mechanics”, “quantum mechanical” and related terms inthis specification refer to a fundamental branch of theoretical physicsthat complements Newtonian mechanics and classical electromagnetism, andoften replaces Newtonian mechanics and classical electromagnetism at theatomic and subatomic levels. Quantum mechanics is the underlyingframework of many fields of physics and chemistry, including condensedmatter physics, quantum chemistry, and particle physics along withgeneral relativity. It is one of the pillars of modern physics. Quantummechanics is a more fundamental theory than Newtonian mechanics andclassical electromagnetism, in the sense that it provides accurate andprecise descriptions for many phenomena “classical” theories simplycannot explain.

The terms “quantum superposition”, superposition and related terms inthis specification refer to a phenomenon of quantum mechanics thatoccurs when an object simultaneously “possesses” two or more values (orstates) of an observable quantity. It is postulated that when theobservable quantity is measured, the values will randomly collapse toone of the superposed values according to a quantum probability formula.The concept of choice (e.g., free will) in a sentient being presupposesthe superposition of possibilities.

The terms “quantum entanglement”, entanglement and related terms in thisspecification refer to a quantum mechanical phenomenon in which thequantum states of two or more objects (including photons and other formsof energy) have to be described with reference to each other, eventhough the individual objects may be spatially separated. Quantumentanglement is the basis for emerging technologies, such as quantumcomputing. Entanglement can be across time or space.

The term “quantum computer” generally refers to any device forcomputation that makes direct use of distinctively quantum mechanicalphenomena, such as superposition and entanglement, to perform operationson data. In this specification, the term “quantum computer” and relatedterms refer to a device that is operable to respond to an influence ofthe mind of a sentient organism (usually a human operator) on quantummechanical wavefunctions. In this specification, the terms “bit”, “bits”and related terms are used broadly to include both classical (orconventional) bits of information and quantum mechanical bits, orqubits.

A qubit is a basic unit of quantum information contained within aphysical entity that embodies a superposition of two states. Ameasurement of the qubit's state collapses the superposition randomly toa determined bit with a value of 1 or 0. Certain influences can causethe probability of the collapsed bit being 1, to be different than 50%.This includes an influence of mind.

An influence of mind can also produce an implicit entanglement betweenthe wavefunction of a qubit and a test number or non-inferableinformation. Such an influence of mind increases the probability thatthe measured state of the qubit will be related to a test number ornon-inferable information.

Non-inferable information is information that is either hidden or cannotbe inferred from presently available information.

A plurality of qubits can be entangled to produce an exponentiallyincreased number of superposed states. All the qubits and theirsuperposed states are collectively subject to an influence of mind sothat when a measurement is made, there is an enhanced probability thatthe measured state of the qubits is related to a test number ornon-inferable information.

A plurality of qubits can be implicitly entangled with each other andwith one or more test numbers or non-inferable information. Theresultant measurements of these qubits' states can be processed byvarious converters such as a cross-correlation converter followed by abias amplifier, and combined to greatly enhance the probability of acorrect relationship in the processed output to the test numbers ornon-inferable information.

Implicit entanglement greatly simplifies the construction of theassembly of qubits. Usually, the requirement of quantum coherencebetween the qubits is met by extremely rigorous control of physicalstructure and environment of the quantum circuit that embodies thequbits. Implicit entanglement caused by an influence of mind canpartially entangle physical sources of entropy, which only contain acomponent of quantum mechanical superposition. The entanglement can spanboth distance and temporal displacement, and exist under conditions thatwould normally destroy any useful quantum coherence.

A programming input can be used to alter the wavefunction of one or morequbits. The signal supplied to the programming input can be derived fromthe measured states of other qubits or from a conditional processedsignal. A conditional, or non-final, signal is produced from a previousone or more measurements and processing. This provides a means ofenhancing both the accuracy and speed of providing a final processedoutput representative of an influence of mind.

The term “general computer” in this specification is used broadly torefer to a conventional computer, which typically has an input device(e.g., a keyboard), a central processing unit (CPU), memory, and aresults interface (e.g., screen, printer). Examples include conventionaldesktop, laptop and some handheld devices.

FIG. 1 contains a block diagram representing a general embodiment of ananomalous effect detector 100 in accordance with the invention.Anomalous effect detector 100 includes a source of non-deterministicrandom numbers 104. An exemplary source of non-deterministic randomnumbers comprises an independent oscillator random number generator(RNG). U.S. Pat. No. 6,862,605, issued Mar. 1, 2005, to Wilber, which ishereby incorporated by reference as if fully contained herein, teachesan independent oscillator device and a method of generating randomnumbers. U.S. Pat. No. 6,324,558, issued Nov. 27, 2001, to Wilber, whichis hereby incorporated by reference, teaches a random number generator(RNG) circuit connected to a general-purpose computer. The RNG circuitincludes a flat source of white noise, and the circuit is powered by thecomputer. Examples of random number generators are described in detailbelow. In some embodiments of a detector 100, source 104 ofnon-deterministic random numbers includes a random noise source 106. Awide range of noise sources are suitable to provide random numbers in asource of non-deterministic random numbers in accordance with theinvention. Examples include components exhibiting thermal noise and shotnoise. Examples of shot noise sources include sources of electronicnoise and photonic noise.

Source 104 of non-deterministic random numbers is operable to generatenon-deterministic random numbers 110. In some embodiments of ananomalous effect detector, the source of non-deterministic randomnumbers is operable to generate non-deterministic random binary bits ata total rate exceeding one billion bits per second. In some embodiments,source 104 is operable to generate an analog non-deterministic randomsignal. In some embodiments, source 104 is operable to generatenon-deterministic random numbers 110 having a bias less than 10 ppm andan autocorrelation less than 10 ppm for any order of autocorrelation. Insome embodiments, source 104 is operable to generate non-deterministicrandom numbers 110 having a bias less than 1 ppm and an autocorrelationless than 1 ppm for any order. In some embodiments, source 104 ofnon-deterministic random numbers is located in an integrated circuit. Insome embodiments, source 104 of non-deterministic random numberscomprises an independent ring oscillator. In some embodiments, source104 of non-deterministic random numbers comprises a single electrontransistor random source. In some embodiments, source 104 ofnon-deterministic random numbers comprises a summed multisource RNG.

As depicted in FIG. 1, anomalous effect detector 100 also includes aconverter 114. A converter 114 is operable to accept an input 110 ofnon-deterministic random numbers from source 104 and to convert aproperty of non-deterministic random numbers 110 into a converter output118. Converter 114 typically comprises one or more converters selectedfrom a group including: a bias amplifier, an autocorrelation converter,a cross-correlation converter, a runs converter, a transitionsconverter, a mutual bias converter and a pattern correlation converter.A bias amplifier typically is operable to amplify bias of an input ofnon-deterministic random numbers. In some embodiments, a bias amplifieris operable to perform a bounded random walk. When a low amount of biasis present in the numbers, then a bias amplifier has a high statisticalefficiency, approaching 1.0. For example, when a low amount of bias ispresent in the numbers, and if the bias amplifier uses one million inputbits to produce one output bit, the bias is amplified by about 1000;that is, the bias is amplified by a factor equivalent to the square rootof the decrease in total numbers. In some embodiments, a bias amplifieris operable to perform a truth table bias function. A patterncorrelation converter uses an arbitrary pattern of bits. Converters arediscussed in more detail below.

Anomalous effect detector 100 further comprises a processor 120.Processor 120 is operable to accept converter output 118 and to producea processor output signal 124 representative of an influence of mind. Afeature of anomalous effect detector 100 is that processor output signal124 contains fewer numbers than input 110 of non-deterministic randomnumbers.

Anomalous effect detector 100 further includes an interface 130 that isoperable to communicate results 124 from processor 120. An example of aresults interface 130 includes: a computer monitor, a computer speaker,a sound transducer, an LED display, a cell phone screen, a cell phonespeaker, a mechanical transducer and a physiological stimulator.

FIG. 2 contains a block diagram representing a more preferred generalembodiment of an anomalous effect detector 140 in accordance with theinvention. Anomalous effect detector 140 includes a source ofnon-deterministic random numbers 144. An exemplary source ofnon-deterministic random numbers comprises an independent oscillatorrandom number generator (RNG). In some embodiments, source 144 ofnon-deterministic random numbers includes a random noise source 146.Source 144 of non-deterministic random numbers also includes arandomness corrector 148 that is operable to accept random numbers (or arandom signal) 149 from noise source 146 and to reduce one or morestatistical defects in the random numbers, thereby reducing statisticaldefects in non-deterministic random numbers 150. Randomness correctorsand correcting randomness are described in more detail below. Source 144of non-deterministic random numbers is operable to generatenon-deterministic random numbers 150. In some embodiments, source 104 isoperable to generate an analog non-deterministic random signal. In someembodiments, source 104 is operable to generate non-deterministic randombinary bits. In some embodiments, randomness corrector 148 is operableto reduce bias in non-deterministic random numbers 150 to less than 10ppm and to reduce autocorrelation of any order in non-deterministicrandom numbers 150 to less than 10 ppm. In some embodiments, randomnesscorrector 148 is operable to reduce bias in the non-deterministic randomnumbers 150 to less than 1 ppm and to reduce autocorrelation of anyorder in non-deterministic random numbers 150 to less than 1 ppm. Insome embodiments, randomness corrector 148 comprises a linear feedbackshift register randomness corrector. In some embodiments, randomnesscorrector 148 comprises a randomness corrector operable to perform afunction selected from the group consisting of: XORing output numbersfrom the source of nondeterministic random numbers with the output of apseudorandom number generator; XORing output numbers from the source ofnondeterministic random numbers with the output of an independentnon-deterministic random number generator; and XORing a number ofconsecutive output numbers of the source of nondeterministic randomnumbers.

As depicted in FIG. 2, anomalous effect detector 140 also includes aconverter 154. Converter 154 is operable to accept an input 150 ofnon-deterministic random numbers from source 144 and to convert aproperty of non-deterministic random numbers 150 into a converter output158. Converter 144 typically comprises one or more converters selectedfrom a group including: a bias amplifier, an autocorrelation converter,a cross-correlation converter, a runs converter, a transitionsconverter, a mutual bias converter and a pattern correlation converter.A bias amplifier typically is operable to amplify bias of an input ofnon-deterministic random numbers. In some embodiments, a bias amplifieris operable to perform a bounded random walk. In some embodiments, abias amplifier is operable to perform a truth table bias function.Converters are discussed in more detail below.

Anomalous effect detector 140 further comprises a processor 160.Processor 160 is operable to accept converter output 158. Anomalouseffect detector 140 further comprises a source 162 of test numbers 163.Processor 160 is operable to measure a relationship between converteroutput 158 in processor 160 and at least one test number 163 from testnumber source 162 to produce a relationship measurement. Processor 160is further operable to abstract a relationship measurement to provide anenhanced output signal 164 representative of an influence of mind. Afeature of anomalous effect detector 140 is that processor output signal164 contains fewer numbers than input 150 of non-deterministic randomnumbers. In some embodiments, a processor 120, 160 comprises a runsconverter operable to convert runs in a relationship measurement in theprocessor into a bias in its output.

Generally, mind is associated with functions of the brain. For thisreason, it is believed that performance improves in embodiments inaccordance with the invention in which number sources and/or informationprocessing nodes are arranged to emulate the processing of neurons inthe brain. In some embodiments in accordance with the invention, aprocessor includes an artificial neural network.

In some embodiments, source 144 of non-deterministic random numbers islocated in an integrated circuit. In some embodiments, source 144 ofnon-deterministic random numbers comprises an independent ringoscillator. In some embodiments, source 144 of non-deterministic randomnumbers comprises a single electron transistor random source. In someembodiments, source 144 of non-deterministic random numbers comprises asummed multisource RNG.

Anomalous effect detector 140 further includes an interface 170 that isoperable to communicate results 164 from processor 160. As depicted inFIG. 2, in some embodiments, interface 170 communicates results to anoperator 180. Anomalous effect detector 140 further comprises aninitiator 190 that is operable to initiate detection by anomalous effectdetector 140 of an influence of mind.

Examples of an initiator 190 include: a keypad, a touchpad, a computerkeyboard, a computer mouse, a microphone, a mechanical transducer, aphoto sensor, a capacitive switch, a touch sensitive screen, aphysiological signal detector and another anomalous effect detector. Insome embodiments, initiator 190 is operable to receive a conditionedphysiological measurement to initiate a detection. In some embodiments,initiator 190 is operable to receive an output from another anomalouseffect detector to initiate a detection. In some embodiments, initiator190 is operable to initiate a detection automatically and periodically.

In some embodiments, source 162 of test numbers is operable to generatetest numbers 163 having a fixed pattern. This feature is useful forassessing and training the ability of an operator to affect directly theproperties of the physical source of non-deterministic random numbers bypsychokinesis. In some embodiments, source 162 of test numbers isoperable to generate at least one test number before initiation ofdetection of an influence of mind. This feature is useful for testingand training clairvoyance abilities of an operator. In some embodiments,source 162 of test numbers is operable to generate at least one testnumber after converting by converter 154 of input 150 to converteroutput 158. This feature is useful for testing and training precognitionabilities of an operator.

Examples of a results interface 170 include: a television, a computermonitor, an LED display, a liquid crystal display, a plasma display, a3-dimentional display, a laser display, an ionized air display, aprojection type display, a sound transducer, a speaker, an earphone, a3-dimentional sound system, an ultrasonic heterodyne transducer, adirect bone sound transducer, an electronic stimulator, a mechanicaltransducer, a direct electronic neural stimulator, a direct photonicneural stimulator or a direct electromagnetic neural stimulator. In someembodiments, the results interface is operable to produce a feedback toan operator within one second of the beginning of the generation of thestream of non-deterministic random numbers. In some embodiments, theresults interface is located remotely from the processor. In someembodiments, the results interface is connected to the processor overthe internet. In some embodiments, the results interface is connected tothe processor via a telephone line. In some embodiments, the resultsinterface is connected to the processor via a wireless connection. Insome embodiments, the results interface comprises a portable device. Insome embodiments, the results interface comprises an internet-enableddevice. In some embodiments, output of processor results from processor160 in results interface 170 function as positive or real-time feedbackto an operator 180. It is understood that in some embodiments, feedbackto an operator does not rise above the subliminal or unconscious level.Examples of subliminal feedback stimuli include: direct neuronalstimulation; electromagnetic stimulation, including light; and othersubliminal sensory stimulation. Unconscious modalities may includesources of stimulation that may become large enough to reach thethreshold of conscious awareness.

Accordingly, some embodiments of an anomalous effect detector inaccordance with the invention further comprise one or more physiologicalsensors, that is, instruments operable to respond to one or morephysiological parameters of an operator 180. Examples of suchinstruments include: a plethysmograph, a photoplethysmograph, animpedance plethysmograph, an oximeter, a respiration monitor, an expiredgas monitor, an electrocardiograph, an electroencephalograph, amagnetoencephalograph, a device for measuring electrodermal response, adevice for measuring skin electrical potentials, an electromyograph anda temperature sensor. Some embodiments further comprise a signalprocessor (not shown) that is responsive to one or more output signalsof one or more physiological sensors. Examples of physiologicalparameters for which corresponding output signals are processed by asignal processor include: heart rate, blood flow, blood perfusion, heartpulse wave velocity, heart rate variability, muscle tension,electroencephalograms, power spectra of electroencephalograms, brainhemisphere ratios in electroencephalogram spectra, electrocardiograms,respiration rate and metabolism. Some embodiments of an anomalous effectdetector in accordance with the invention comprise a results interfaceand a signal processor connected to the results interface, the resultsinterface being operable to present an output corresponding to at leastone of the physiological parameters.

Some embodiments of an anomalous effect detector in accordance with theinvention further comprise a bias input operable to alter a probabilityof a property of the non-deterministic random numbers. Such a bias inputallows probability feedback.

In some embodiments, a converter (e.g., converter 114, 154) comprises: across-correlation converter that is operable to convertcross-correlation between a plurality of simultaneously generatednon-deterministic random numbers into a bias contained in across-correlation converter output; and a bias amplifier that isoperable to amplify bias contained in a cross-correlation converteroutput. In some embodiments, a converter comprises: a runs converterthat is operable to convert runs in the input of non-deterministicrandom numbers into a bias contained in a runs converter output; and abias amplifier that is operable to amplify bias in the runs converteroutput. In some embodiments, a converter comprises: an autocorrelationconverter that is operable to convert autocorrelation in an input ofnon-deterministic random numbers into a bias contained in anautocorrelation converter output; and a bias amplifier that is operableto amplify bias contained in the autocorrelation converter output. Insome embodiments, a converter comprises: a cross-correlation converterthat is operable to convert cross-correlation between a plurality ofsimultaneously generated non-deterministic random numbers into a biascontained in a cross-correlation converter output. In some embodiments,a converter comprises: a cross-correlation converter that is operable toconvert cross-correlation between a plurality of simultaneouslygenerated non-deterministic random numbers into a bias contained in across-correlation converter output; a runs converter that is operable toconvert runs in the cross-correlation converter output into a biascontained in a runs converter output; and a bias amplifier that isoperable to amplify bias contained in the runs converter output. In someembodiments, a converter comprises: a mutual bias converter operable toconvert a mutual bias in a plurality of simultaneously generatednon-deterministic random numbers into a bias contained in a mutual biasconverter output, and a bias amplifier that is operable to amplify biascontained in the mutual bias converter output. Converters are describedin more detail below.

A basic embodiment of a method of detecting an anomalous effectresulting from an influence of mind comprises: providing an input ofnon-deterministic random numbers; converting a property of the input ofnon-deterministic random numbers into a converter output; accepting theconverter output into a processor; and producing in the processor aprocessor output signal representative of the influence of mind, whereinthe processor output signal contains fewer numbers than the input ofnon-deterministic random numbers; and communicating results from theprocessor using an interface. Other embodiments of methods in accordancewith the invention are clear from the descriptions herein. For example,some embodiments of a method of detecting an anomalous effect furthercomprise: providing at least one test number to the processor; measuringa relationship between the converter output in the processor and atleast one test number to produce a relationship measurement; and in theprocessor, abstracting the relationship measurement to provide anenhanced output signal representative of the influence of mind.

An exemplary anomalous effect detector for responding to an influence ofmind and an exemplary method of utilizing an anomalous effect detectorare described in Example 1 below. The exemplary anomalous effectdetector is described with reference to anomalous effect detector 140,depicted in FIG. 2. The exemplary method is described with reference togeneralized method 200 outlined in the process flow sheet of FIG. 3. Itis understood that numerous embodiments of apparatuses, techniques andmethods in accordance with the invention deviate from the particularembodiments described herein.

EXAMPLE 1

Computer programs, referred to herein as detector software, areinstalled in a conventional general-purpose personal computer to provideoperability corresponding to the operability of source 144 ofnon-deterministic random numbers, converter 154, processor 160, testnumber source 162, results interface 170 and initiator 190. The personalcomputer includes a conventional low-frequency (LF) oscillator (orclock) having a frequency of about 1 kHz. The personal computer alsoincludes a conventional high-speed, 64-bit counter. It is a feature ofthis exemplary embodiment that an anomalous effect detector inaccordance with the invention is formed through operation of thedetector software using the usual components of the personal computer.For example, the computer keyboard functions as an initiator device,corresponding to initiator 190 of anomalous effect detector 140 in FIG.2. The computer screen and speaker function as results interfaces,corresponding to results interface 170. The LF oscillator, thehigh-speed counter, and the personal computer's conventional memory andprocessor together with true random number generator (TRNG) programsincluded in the detector software function as a noise source and arandomness corrector, corresponding to noise source 146 and randomnesscorrector 148 of source 144 of non-deterministic random numbers inanomalous effect detector 140. Similarly, the LF oscillator, thehigh-speed counter, and the personal computer's conventional memory andprocessor together with the detector software function to generatenon-deterministic random test bits, corresponding to the function ofsource 162 of test numbers of anomalous effect detector 140. Thepersonal computer's conventional memory and processor together withprocessing programs included in the detector software function as aprocessor, corresponding to processor 160 of anomalous effect detector140 of FIG. 2.

In step 210 of method 200, the operator initiates a detection of aninfluence of mind with a press of a keyboard key or with a mouse click.In step 220, the next edge of the low-speed oscillatory signal of thecomputer's low-speed oscillator causes reading of the word contained inthe high-speed counter. Then, bits containing entropy are extracted fromthe read word. Processes of step 220 are conducted substantially asfollows. From the word read, the lower (i.e., the least significant)bits are selected. The number of these lower bits is approximately thenumber of bits that have changed since the previous read. In thisexemplary embodiment in a 4 GHz personal computer using a 1 kHz samplingrate, about 22 bits change in the one millisecond between samples.

In step 230, bits containing entropy are processed by a randomnesscorrector to produce a non-deterministic random bit. The processes ofstep 230 are performed substantially as follows: the selected lower bits(the bits containing entropy) are XOR-ed with an equal number of bitsfrom a pseudorandom number generator; the resultant 22 bits are allXOR-ed together to produce a single output bit containing almost 1.0 bitof entropy. The single output bit corresponds to one of the bits instream 150 of non-deterministic random bits, as depicted in FIG. 2.Software-based pseudorandom generators are known in the art.

In steps 240, 250, and 254 a single output bit of stream 150 isconverted in an iterative process that corresponds to the processing ofconverter 154 of anomalous effect detector 140. In step 240 of a firstiteration of steps 240, 250, and 254, one non-deterministic random bitis XOR-ed with a pseudorandom (deterministic) bit to produce aquasi-random bit. In step 250, the quasi-random bit is processed in abias amplifier comprising a bounded random walk. In this exemplaryembodiment, the bounded random walk is a 1-dimensional bounded randomwalk, with two steps to each boundary. If the bounded random walkterminates at one designated boundary, then the result is “1”. If thebounded random walk terminates at the other designated boundary, thenthe result is “0”. Some alternative embodiments utilize amulti-dimensional bounded random walk. A bounded random walk is amathematical technique that amplifies bias in a group of numbers. Instep 254, if an output bit has been produced, it is accumulated in step256. If an output bit has not been produced, the iterative process ofsteps 240, 250, and 254 is repeated. In a second and in furtheriterations, the one random bit is XOR-ed once again with a pseudorandom(deterministic) bit to produce a quasi-random bit. In step 250, theresulting sequence of quasi-random bits (i.e., the sequence of thequasi-random bit from step 240 of the first iteration plus thequasi-random bit resulting from step 240 in each subsequent iteration)is processed in a bias amplifier comprising a bounded random walk. Againin step 254, if an output bit has been produced, it is accumulated instep 256. If an output bit has not been produced, the iterative processof steps 240, 250, and 254 is repeated. An average of four iterations isperformed to produce an output bit in step 250. A minimum of twoiterations is necessary to produce an output bit in step 250. The numberof iterations to produce an output bit occasionally reaches 20 to 30iterations, but seldom exceeds 30 iterations. In this exemplaryembodiment using a 1 kHz LF oscillator, a non-deterministic random bitis processed in the iterative bias-amplifying conversion process ofsteps 240, 250 and 254 at a rate of about 1000 bits per second toproduce an output bit at a rate of about 1000 bits per second.

A step 256 includes accumulating 111 bias amplifier output bits. Step258 includes determining if 111 bias amplifier output bits haveaccumulated. If not, processes 220 through 256 are repeated, as depictedin FIG. 3. If 111 bias amplifier output bits have accumulated, then step260 is performed. Bias amplifier output bits correspond approximately toconverter output 158 depicted in FIG. 2.

Step 260 includes comparing 111 bias amplifier output bits with 111binary test bits. For each match, a “1” is generated. For each mismatch,a “0” is generated. Step 270 includes performing a majority vote basedon the number of bias amplifier output bits that match a correspondingtest bit. Steps 260 and 270 correspond to operations performed byprocessor 160 of anomalous effect detector 140 using test numbersprovided by test number source 162. In more general terms, step 260comprises measuring a relationship (i.e., match or mismatch) between theoutput of a converter (i.e., 111 output bits from bias amplifying) and acorresponding number of test numbers. In general terms, step 270comprises abstracting the measured relationship by reducing 111 databits to a single majority vote result.

Step 280 includes communicating the result of the majority vote to acomputer monitor and a computer speaker, which correspond to resultsinterface 170 of anomalous effect detector 140. The result of themajority vote is a “Hit” if the number of matches in step 270 is greaterthan 55. Otherwise, the result is a “Miss”. Step 280 typically includesdisplaying the result of the comparison of step 270 to an operator 180(FIG. 2).

FIG. 4 depicts a block diagram of a system 300 that comprises ananomalous effect detector in accordance with the invention and that isoperable to practice a method in accordance with the invention ofdetecting an influence of mind. System 300 includes regulated powersupplies 310, which are operable to provide electric power to a FPGA 320(field programmable gate array). System 300 further comprisesconventional general-purpose personal computer 330 and I/O(input/output) interface 340, which is operable to provide an interfacebetween FPGA 320 and personal computer 330.

FPGA 320 is designed to provide to personal computer 330 operabilitycorresponding to the operability of source 144 of non-deterministicrandom numbers, converter 154, processor 160 and test number source 162.Results interface 170 and initiator 190 are provided in personalcomputer 330. Some embodiments of a system in accordance with theinvention include a different gate array device instead of or inaddition to a FPGA. Examples of suitable gate arrays include an ASIC anda custom IC (integrated circuit).

FIG. 5 depicts a block diagram of a quantum computer 400 in accordancewith the invention for responding to an influence of mind. Quantumcomputer 400 comprises a physical source of entropy 404 operable togenerate output numbers 405. Quantum computer 400 is operable to usequantum effects to generate results 414 representative of an influenceof mind. A wide range of entropy sources are suitable to provide entropycontained in a physical source of entropy in accordance with theinvention. Examples include thermal noise and shot noise. Examples ofshot noise sources include sources of electronic noise and photonicnoise. Examples of quantum mechanical entropy sources include: spindirection, photon polarization, nuclear decay, state transition timing,a photon beam splitter, and a single-electron transistor. In someembodiments, physical source of entropy 404 is located in an integratedcircuit. In some embodiments, physical source of entropy 404 comprisesan independent ring oscillator. In some embodiments, physical source ofentropy 404 comprises a single electron transistor random source. Insome embodiments, physical source of entropy 404 comprises a quantumcircuit embodying a qubit. In some embodiments, physical source ofentropy 404 comprises a quantum circuit embodying at least one qubit,which qubit exhibits a property of quantum entanglement. In someembodiments, physical source of entropy 404 comprises an independentoscillator random number generator (RNG). U.S. Pat. No. 6,862,605,issued Mar. 1, 2005, to Wilber teaches an independent oscillator deviceand a method of generating random numbers.

Quantum computer 400 further comprises a source 406 of test numbers 407.Quantum computer 400 further comprises a measurement processor 410.Measurement processor 410 is operable to accept output numbers 405 fromentropy source 404. Measurement processor 410 is further operable tomeasure a relationship between process numbers in measurement processor410 and at least one test number 407 to produce an output 414representative of an influence of mind. Accordingly, measurementprocessor 410 comprises a relationship processor 418, which is operableto measure a relationship between process numbers in measurementprocessor 410 and at least one test number 407. In some embodiments, ameasurement processor 410 comprises a runs converter operable to convertruns in a relationship measurement in the processor into a bias.

Quantum computer 400 further comprises a results interface 420 that isoperable to communicate results from measurement processor 410. Anexample of a results interface 420 includes: a computer monitor, acomputer speaker, a sound transducer, an LED display, a cell phonescreen, a cell phone speaker, a mechanical transducer and aphysiological stimulator.

In some embodiments, entropy source 404 is operable to generate digitaloutput numbers. In some embodiments, source 404 is operable to generatean analog output signal. In some embodiments, physical source of entropy404 is located in an integrated circuit. In some embodiments, physicalsource of entropy 404 comprises an independent ring oscillator. In someembodiments, physical source of entropy 404 comprises a single electrontransistor random source. In some embodiments, physical source ofentropy 404 comprises a quantum circuit embodying a qubit. In someembodiments, physical source of entropy 404 comprises a quantum circuitembodying at least one qubit, which qubit exhibits a property of quantumentanglement. In some embodiments, physical source of entropy 404comprises a summed multisource RNG.

An exemplary relationship processor 418 is operable to measure arelationship between process numbers in measurement processor 410 andtest numbers 407. In some embodiments, as explained above in Example 1with reference to method 200 of FIG. 3, process numbers are directlycompared to test numbers; then, a majority vote of the comparisonresults is performed. In some embodiments, process numbers are directlycompared to test numbers; then, a runs analysis is performed on asequence of bits resulting from the comparison. In some embodiments, across-correlation is performed between process numbers and test numbers.

FIG. 6 depicts a block diagram of a preferred embodiment of quantumcomputer 430 in accordance with the invention for responding to aninfluence of mind. Quantum computer 430 comprises a physical source ofentropy 434 operable to generate output numbers 435. Quantum computer430 is operable to use quantum effects to generate output numbers 435. Awide range of entropy sources are suitable to provide entropy containedin a physical source of entropy in accordance with the invention. Insome embodiments, source 434 is operable to generate output numbers 435having a bias less than 10 ppm and an autocorrelation less than 10 ppmfor any order of autocorrelation. In some embodiments, source 434 isoperable to generate output numbers 435 having a bias less than 1 ppmand an autocorrelation less than 1 ppm for any order. In someembodiments, physical source of entropy 434 is located in an integratedcircuit. In some embodiments, physical source of entropy 434 comprisesan independent ring oscillator. In some embodiments, physical source ofentropy 434 comprises a single electron transistor random source. Insome embodiments, physical source of entropy 434 comprises a quantumcircuit embodying a qubit. In some embodiments, physical source ofentropy 434 comprises a quantum circuit embodying at least one qubit,which qubit exhibits a property of quantum entanglement. In someembodiments, physical source of entropy 434 comprises an independentoscillator RNG. In some embodiments, physical source of entropy 434comprises a summed multisource RNG.

Quantum computer 430 further comprises a source 436 of test numbers 437.Quantum computer 430 further comprises a measurement processor 440.Measurement processor 440 is operable to accept output numbers 435 fromentropy source 434. Measurement processor 440 is further operable tomeasure a relationship between process numbers in measurement processor440 and at least one test number 437 to produce an output 444representative of an influence of mind. Measurement processor 440includes a converter 446 that is operable to convert output numbers 435from the physical source of entropy 434 to a converter output 437representative of a property of the output numbers. In some embodiments,a measurement processor 440 comprises a runs converter operable toconvert runs in a relationship measurement in the processor into a bias.Measurement processor 440 further includes a relationship processor 448,which is operable to measure a relationship between output numbers 447from converter 446 and at least one test number 437. Quantum computer430 further comprises a results interface 450 that is operable tocommunicate results 444 from measurement processor 440. As depicted inFIG. 6, in some embodiments, interface 450 communicates results to anoperator 460. Quantum computer 430 further comprises an initiator 470that is operable to initiate a detection of an influence of mind.

In some embodiments, entropy source 434 is operable to generate digitaloutput numbers. In some embodiments, source 434 is operable to generatean analog output signal.

Converter 446 typically comprises one or more converters selected from agroup including: a bias amplifier, an autocorrelation converter, across-correlation converter, a runs converter, a transitions converter,a mutual bias converter and a pattern correlation converter. A biasamplifier typically is operable to amplify bias of output numbers fromentropy source 434. In some embodiments, a bias amplifier is operable toperform a bounded random walk. In some embodiments, a bias amplifier isoperable to perform a truth table bias function.

In some embodiments, a converter 446 comprises: a cross-correlationconverter that is operable to convert cross-correlation between aplurality of simultaneously generated output numbers 435 from physicalsource of entropy 434 into a bias contained in a cross-correlationconverter output; and a bias amplifier that is operable to amplify biascontained in the cross-correlation converter output. In someembodiments, a converter 446 comprises: a runs converter that isoperable to convert runs in output numbers 435 from physical source 434of entropy into a bias contained in a runs converter output; and a biasamplifier that is operable to amplify bias in the runs converter output.In some embodiments, a converter 446 comprises: an autocorrelationconverter that is operable to convert autocorrelation in output numbers435 from physical source of entropy 434 into a bias contained in anautocorrelation converter output; and a bias amplifier that is operableto amplify bias contained in the autocorrelation converter output. Insome embodiments, a converter 446 comprises: a cross-correlationconverter that is operable to convert cross-correlation between aplurality of simultaneously generated output numbers 435 from a physicalsource of entropy 434 into a bias contained in a cross-correlationconverter output. In some embodiments, a converter 446 comprises: across-correlation converter that is operable to convertcross-correlation between a plurality simultaneously generated outputnumbers 435 from a physical source 434 of entropy into a bias containedin a cross-correlation converter output; a runs converter that isoperable to convert runs in the cross-correlation converter output intoa bias contained in a runs converter output; and a bias amplifier thatis operable to amplify bias contained in the runs converter output. Insome embodiments, a converter comprises: a mutual bias converteroperable to convert a mutual bias in a plurality of simultaneouslygenerated output numbers 435 into a bias contained in a mutual biasconverter output; and a bias amplifier that is operable to amplify biascontained in the mutual bias converter output. Converters are discussedin more detail below.

In some embodiments, source 436 of test numbers is operable to generatetest numbers 437 having a fixed pattern. This feature is useful forassessing and training the ability of an operator to affect directly theproperties of the physical source of non-deterministic random numbers bypsychokinesis. In some embodiments, source 436 of test numbers isoperable to generate at least one test number before initiation ofdetection of an influence of mind. This feature is useful for testingand training clairvoyance abilities of an operator. In some embodiments,source 436 of test numbers is operable to generate at least one testnumber 437 after converting by converter 446 of output numbers 435 toconverter output 447. This feature is useful for testing and trainingprecognition abilities of an operator.

An exemplary relationship processor 448 is operable to measure arelationship between process numbers in measurement processor 440 andtest numbers 437. In some embodiments, as explained above in Example 1with reference to method 200 of FIG. 3, process numbers are directlycompared to test numbers; then, a majority vote of the comparisonresults is performed. In some embodiments, process numbers are directlycompared to test numbers; then, a runs analysis is performed on asequence of bits resulting from the comparison. In some embodiments, across-correlation is performed between process numbers and test numbers.

Some embodiments of a quantum computer 430 further comprise aprogramming input 480 operable to alter a probability function ofphysical source of entropy 434. A programming input can be used to alterthe wavefunction of one or more qubits. In some embodiments, the signalsupplied to the programming input is derived from the measured states ofother qubits or from a conditional processed signal. A conditional, ornon-final, signal is produced from a previous one or more measurementsand processing. This provides a means of enhancing both the accuracy andspeed of providing a final processed output representative of aninfluence of mind.

An example of a technique to alter a probability function of a physicalsource of entropy is to adjust the threshold level of converting ananalog signal to binary. This changes the probability of getting a “1”or “0”. Another example is to change the duty cycle of a square wavebeing sampled; for example the square wave of a ring oscillator. Anotherexample of altering a probability function is to adjust autocorrelationby adjusting the transfer function of the filter through which a signalis passing.

Examples of an initiator 470 include: a keypad, a touchpad, a computerkeyboard, a computer mouse, a microphone, a mechanical transducer, aphoto sensor, a capacitive switch, a touch sensitive screen, aphysiological signal detector and another anomalous effect detector. Insome embodiments, initiator 470 is operable to receive a conditionedphysiological measurement to initiate a detection. In some embodiments,initiator 470 is operable to receive an output from another anomalouseffect detector to initiate a detection. In some embodiments, initiator470 is operable to initiate a detection automatically and periodicallyonce started.

In some embodiments, quantum computer 430 also includes a randomnesscorrector that is operable to accept output numbers 435 (e.g., digitalnumbers or an analog signal) from entropy source 434 and to reduce oneor more statistical defects in the output numbers. Randomness correctorsand correcting randomness are described in more detail below. One ormore techniques are applied for reducing defects in an analog signal.For example, in some embodiments, physical source of entropy 434produces an analog output 435 in which the average voltage is not zero.This defect is reduced by using a negative feedback to cancel out along-term average voltage. This is a simple technique and leaves thesignal intact. Another technique used to reduce defects is to multiplythe analog signal by a random sequence of “1s” and “minus 1s”. Thisreduces autocorrelation and bias, but does not leave the wave signalintact. Another technique is to multiply the signal by a pseudorandomlygenerated analog wave having perfect statistics. Then, the absolutevalue of the analog signal is calculated, and then the square root ofthe absolute value is calculated. Finally, the positive and negativesigns of the original signal wave sections are re-applied to get asignal with reduced defects.

In some embodiments, a randomness corrector in quantum computer 430 isoperable to reduce bias in output numbers 435 to less than 10 ppm and toreduce autocorrelation of any order in non-deterministic random numbers435 to less than 10 ppm. In some embodiments, a randomness corrector isoperable to reduce bias in output numbers 435 to less than 1 ppm and toreduce autocorrelation of any order in non-deterministic random numbers435 to less than 1 ppm. In some embodiments, a randomness correctorcomprises a linear feedback shift register randomness corrector. In someembodiments, a randomness corrector comprises a randomness correctoroperable to perform a function selected from the group consisting of:XORing output numbers from the physical source of entropy with theoutput of a pseudorandom number generator; XORing output numbers fromthe physical source of entropy with the output of an independentnon-deterministic random number generator; and XORing a plurality ofconsecutive output numbers of the physical source of entropy.

A basic embodiment of a method of using a quantum computer that isresponsive to an influence of mind comprises: generating output numbersusing a physical source of entropy; providing at least one test number;accepting the output numbers in a measurement processor; measuring arelationship between process numbers in the measurement processor and atleast one test number to produce an output representative of aninfluence of mind; and communicating results from the measurementprocessor using an interface. Other embodiments of a method of using aquantum computer are clear from the descriptions of the quantum computerherein.

FIG. 7 contains a block diagram illustrating a general embodiment of amulti-stream RNG 500 in accordance with the present invention.Multi-stream RNG 500 includes noise diode 510, which is a source ofrandom noise operable to generate an analog signal 512. Types of devicessuitable for use as a source 510 of random noise include, for example, anoise diode, a zener diode, a photodiode, an avalanche diode, asemiconductor junction, a resistor and a radiation detector.

Multi-stream RNG 500 further includes analog-to-digital converter (ADC)520. ADC 520 is operable to convert analog signal 512 to n number of ADCoutput lines 522, wherein n≧2. As depicted in FIG. 7, ADC 520 has n=8output lines 522. Typically, multi-stream RNG 500 further includes oneor more amplifiers 524 to adjust the amplitude of analog noise signal512 provided by noise source 510 before the noise signal enters ADC 520.The amplified noise signal preferably has an average peak-to-peakamplitude about equal to the full-scale input range of ADC 520.Preferably, the analog signal 512 of noise source 510 and ADC 520together have a full-power bandwidth about two times the samplingfrequency of ADC 520. In other words, the total transfer function of theanalog signal path preferably has a full power bandwidth of twice thesampling frequency of ADC 520.

Depending on the intended use of a multi-stream RNG and of the sequencesof random numbers it generates, the minimum amount of entropy in thesequences from each of the output lines is optimized by selecting thenumber m of output lines that are corrected in randomness correctors.Preferably, selection of the number m of output lines to be correctedfrom the total number n of ADC output lines is conducted throughmathematical modeling and simulation in accordance with the invention.The number of lines n to be used can be determined by theoreticallymodeling the cross-correlation matrix of all the output lines whilevarying the RMS or peak-peak amplitude of the of the ADC input signalrelative to the full-scale input range of the ADC. The cross-correlationincreases for the pairs of more significant bits. The cross-correlationis related to the mutual entropy in the pairs of sequences, so the inputamplitude and the number of less-significant bits is adjusted to achievethe desired level of independent entropy in the selected number ofsequences n.

Non-deterministic random bits generated in accordance with the inventionhave true entropy. As a result, random data generated in accordance withthe invention are able to be influenced by mind.

Exemplary commercially-available noise diodes suitable for use as anoise source 510 in a random number generator in accordance with theinvention include: NoiseCom NC 302LBL; Panasonic MAZ80620ML. Anexemplary amplifier suitable for use as an amplifier 524 is a MAR-65Mavailable from Microcircuits. Exemplary analog-to-digital converterssuitable for use as an ADC 520 include: ADC 08200 CIMT (200 MHz) and ADC081000 (1 GHz), both available from National Semiconductor.

FIG. 8 contains a block diagram of a self-seeding randomness corrector540 suitable for use with a random number generator in accordance withthe invention, such as multi-stream random number generator 500.Randomness corrector 540 typically comprises data input 542 to receivedata 543 (e.g., an uncorrected sequence of bits); for example, data froman output line 522 of ADC 520. Randomness corrector 540 also includesserial shift register (SSR) 550 having a plurality of latches 552 and aplurality of shift intervals 554 (e.g., L1, L2, L3, L4). Randomnesscorrector 540 includes a plurality of parallel data taps 558, each datatap located at a latch. Randomness corrector 200 further comprises anonlinear combining element 560, which is operable to accept data 543via input 542, to accept data from a plurality of parallel data taps558, to combine input data 543 and the data 562 from parallel data taps558 into a corrected bit 564, and for inputting corrected bit 564 intoan input of SSR 550. A data clock 568 is operable to shift data throughrandomness corrector 200. As data is clocked by data clock 568 duringoperation, typically the following occurs: a corrected data bit 564 issampled (read) in line 570 from the nonlinear combining element 560;corrected bit 564 enters SSR 550; a new data bit 543 is input intononlinear combining element 560; and data bits 562 from parallel datataps 558 move into nonlinear combining element 560.

Each shift interval of SSR 550 corresponds to a predetermined number ofbit shifts. In preferred embodiments, randomness corrector 540 isoperable to sample a plurality of parallel SSR output signals from aplurality of sampling data taps that are separated from each other byrelatively prime shift intervals.

In some embodiments, a plurality of data taps are connected to an inputof a nonlinear combining element, the plurality of connected data tapsbeing separated from each other by relatively prime shift intervals. Insome embodiments, a randomness corrector comprises a plurality of shiftregisters connected in series.

Examples of devices that are suitable to function as nonlinear combiningelement 560 include: an Exclusive-Or (XOR) gate, a parity generator, abinary adder with carry, a binary subtracter with borrow, a look-uptable, or a pseudo-random number generator.

An exemplary randomness corrector 530 comprises an FPGA having partnumber EP1C3T144C6, available from Altera.

FIG. 9 depicts schematically an XOR gate 580 suitable for use inaccordance with the invention. As depicted in FIG. 9, a data input (DI)line and a plurality of parallel SSR output (D1-D4) signals enter intoXOR gate 580, which produces an output data bit 564.

FIG. 10 depicts schematically parity generator 590 suitable for use inaccordance with the invention. As depicted in FIG. 10, a data input(DI1) line 543 and a plurality of parallel SSR output (D1-D4) signals562 enter into parity generator 590, which produces an output data bit564.

FIG. 11 contains a block diagram of another self-seeding randomnesscorrector 600 suitable for use with a random number generator inaccordance with the invention, such as multi-stream random numbergenerator 500. Randomness corrector 600 comprises a nonlinear combiningelement 602, which is operable to accept data (D1-D4) from a pluralityof parallel data taps 558 of a serial shift register (SSR) 550 and tocombine the data (D1-D4) from parallel data taps 558 of latches 552 intoa line 606, which is input for nonlinear combining element 610. Theoutput 612 of randomness corrector 600 serves as alternate data output614 of a corrected sequence of random bits. Nonlinear combining element610 is operable to accept input data 543 via input 542 (e.g., from aselected one of the output lines 522 of ADC 520), and to accept datafrom line 616, for the purpose of combining input 543 (e.g., the oneselected ADC output line) and the data from line 616 into a correctedsequence of bits 618, and for inputting the corrected sequence 618 ofbits into an input of SSR 550. In an exemplary randomness corrector 600,all logic is implemented inside an Altera FPGA EP1C3T144C6. As describedwith reference to FIG. 11, SSR 550 contains four shift intervals 554. Ithas been observed that four shift intervals is a minimum number of shiftintervals for good results. It is understood, however, that someembodiments in accordance with the invention include more than fourshift intervals. It is also understood that in some embodiments inaccordance with the invention, a SSR 550 in a randomness corrector 600contains only two or three shift intervals.

FIG. 12 contains a block diagram of another self-seeding randomnesscorrector 630 suitable for use with a random number generator inaccordance with the invention, such as multi-stream random numbergenerator 500. Randomness corrector 630 typically comprises data input632 to receive data 633 (e.g., an uncorrected sequence of bits); forexample, data from an output line 522 of ADC 520. Randomness corrector630 also includes n-bit serial shift register (SSR) 640, which is aserial-in—parallel-out type shift register. Randomness corrector 630includes a plurality of parallel data taps (not shown), each data taplocated at a latch (similar to data taps 558 and latches 552 depicted inFIGS. 8 and 11). Randomness corrector 630 further comprises apseudo-random number generator (PRNG) 644 and a data clock 646. Uponclocking of data clock 646, SSR 640 is operable to produce an outputword 648 containing n bits as an input for PRNG 644. Randomnesscorrector 630 further comprises a nonlinear combining element 650, whichis a parity generator. Parity generator 650 is operable to accept data633 via input 632 and to accept data 652 from PRNG 644. Randomnesscorrector 630 is operable so that a single corrected data bit is sampledor an n-bit quasi-random word is sampled at data lines 656.

In some embodiments, a randomness corrector in accordance with theinvention is operable to generate one or more bits of output 570, 612,656 for every input bit at input 542, 632. In accordance with theinvention, a data clock 568, 646 is operable to clock a plurality oftimes for every bit input. For example, if a clock clocks four times forevery bit of input, the randomness corrector generates four correctedbits out for each input bit. In such a case, each output bit has aboutone fourth of the original entropy; i.e., the output bit isquasi-random.

In some embodiments, a randomness corrector comprises k number of serialshift registers (SSRs), wherein k>m (m being the number of selected ADCoutput lines or other independent sources of random numbers), each shiftregister having a plurality of latches and a plurality of shiftintervals. Furthermore, each shift register includes a plurality ofparallel sampling data taps that are relatively prime to each other.Such a randomness corrector includes a nonlinear combining element foreach of the k shift registers, the nonlinear combining element beingoperable to accept data from a selected one of the m ADC output lines,to accept data from a plurality of parallel data taps, to combine theone selected ADC output line and the data from parallel data taps into acorrected output bit, and to input the corrected output bit into aninput of the SSR. A data clock is operable to clock data through therandomness corrector.

In some embodiments of a random number generator in accordance with theinvention, an ADC is operable so that the n number of ADC output lineshave an aggregate bit rate of n times the sampling frequency of the ADC.In some embodiments, the ADC is operable so that the n number of ADCoutput lines have an aggregate bit rate greater than one billion bitsper second. In some embodiments, m number of randomness correctors areoperable so that m number of corrected sequences of bits have anaggregate bit rate greater than one billion bits per second. In someembodiments, m number of randomness correctors are operable so that mnumber of corrected sequences of bits have an aggregate bit rate greaterthan six billion bits per second. In some embodiments, m number ofrandomness correctors are operable so that m number of correctedsequences of bits have an aggregate bit rate greater than twelve billionbits per second.

FIG. 13 shows a simplified block diagram of a summed multisource randomnumber generator 660 in accordance with the invention having a pluralityof independent noise sources 662 of random noise coupled at a summingpoint 664 and being operable to generate a signal 665 at the summingpoint. Examples of independent sources of random noise include: a noisediode, a zener diode, a photodiode, an avalanche diode, asuperconductive junction, a resistor and a radiation detector. Someembodiments further comprise a coupler 668 and 669, the coupler beingconnected to the sources of random noise and being operable to couplethe sources of random noise at summing point 664. In some of theseembodiments, the coupler is operable to couple the sources of randomnoise using electromagnetic fields. Examples of a coupler include: awire, a resistor, a variable resistor, a capacitor, a variablecapacitor, an optically controlled resistor, an electronicallycontrolled resistor, a photodiode, a converter, a delay line and acontrollable delay line.

Summed multisource RNG 660 includes bias voltages 666 and bias resistors667. Exemplary bias resistors have a resistance of 1.2 kilo-ohms (kΩ).Weighting resistors 668 have a resistance in a range of about from zeroto 100 ohms. The resistance R1, R2 and R3 of three weighting resistors668 are different so that noise sources 662 are weighted differently.The three capacitors 669 having a value of about 1 microfarad (pF) serveto decouple the DC components of noise diodes 662 from each other andfrom the input of amplifier 670. Amplifier 670 is located betweensumming point 664 and analog to digital converter (ADC) 672 and isoperable to adjust signal 665 to signal 671.

ADC 672 has at least one ADC output line 674 and is responsive to theamplified signal 671 to produce random numbers in at least one ADCoutput line 674. In exemplary embodiments, non-deterministic randomnumbers produced by summed multisource RNG 660 serve as an input to aconverter.

Some embodiments comprise at least three independent sources of randomnoise and are characterized in that the at least three independentsources of random noise are spatially arranged to form one or moretriangles. In some embodiments, the independent sources of random noiseare part of a monolithic integrated circuit. In some embodiments, theindependent sources of random noise are all contained within a sphericalradius of 0.5 mm. In some embodiments, the independent sources of randomnoise are coupled to simulate neuronal connections in a brain. In someembodiments, one or more output lines 674 of summed multisource randomnumber generator 660 are the input of at least one randomness correctorthat is operable to process the at least one ADC output line 674.

In some embodiments, a source of non-deterministic random numberscomprises an independent ring oscillator. In some embodiments, aphysical source of entropy comprises an independent ring oscillator.

A ring oscillator is formed by connecting an odd number of invertinglogic gates in a ring. The frequency of oscillation is proportional tothe inverse of the number of logic gates used in the ring.

FIG. 14 depicts schematically a simple ring oscillator random numbergenerator (RNG) 700. Ring oscillator RNG 700 comprises inverting logicgates 702, 704 and 706 connected in a ring 708. Ring oscillator randomnumber generator 700 further comprises a data latch 710 and a clocksource 712. Ring oscillator 708 produces a high-frequency signal (F1)716, which is applied to the data input 718 of data latch 710. Clocksource 712 supplies a clock signal (F3) 720 to the clock input of latch710. The frequency of clock signal (F3) 720 is lower than high-frequencysignal (F1) 716. Random bits 724 are latched out on the Q output 724 ofthe latch at the clock signal frequency.

The entropy of the random sequence produced by ring oscillator RNG 700depicted in FIG. 14 arises from the analog components that comprise theinverting gates 702, 704 and 706. Noise sources in these componentsinclude shot noise and thermal noise, as well as noise induced by powersupply variations. The noise sources produce small variations in thetiming of the rise and fall of the oscillator signal. These variationsare known as transition jitter, or just jitter.

The amount of entropy is related to the root-mean-square (RMS) jitter asa fraction of the ring oscillator period. The jitter in an integratedcircuit ring oscillator is only about 1 percent of the oscillator periodfor oscillator frequencies of several hundred MHz. Therefore, theentropy of a simple ring oscillator RNG 700 of FIG. 14 is also small.

FIG. 15 shows an enhanced ring oscillator RNG 730. Ring oscillator RNG730 comprises inverting logic gates 702, 704 and 706 connected in a ring708. Ring 708 produces a high-frequency signal (F1) 716, which isapplied to the data input 718 of data latch 710. Ring oscillator RNG 730further comprises inverting logic gates 732, 734 and 736 andnon-inverting logic gate 738 in a second ring oscillator 740. Secondring oscillator 740 produces a high-frequency signal (F2) 742 at a lowerfrequency than signal 716 (F1). Second ring oscillator 740 supplies asignal 742 (F2) to the clock input of latch 710. The combined jitter ofthe two ring oscillators 708 and 740 increases the total entropy atoutput 744 of first latch 710. The output 745 of first latch 710 is inturn latched by a clock signal (F3) 720 of clock source 712 at afrequency lower than either signal F1 or F2. In some embodiments, clock712 is a system clock so that the random bits produced by the embodimentof ring oscillator RNG 730 are synchronized with a specific clock.

In some embodiments, it is desirable that the entropy of the random bitsbe greater than the entropy of bits produced by a ring oscillator RNG700 or 730. In an anomalous effect detector or a quantum computer inaccordance with the invention, good results are obtained with entropyvalues of at least 0.9 bits per bit, up to 0.99 bits per bit. Entropyvalues above 0.99 have little incremental benefit. Combined RMS jittervalues of 20-30 percent correspond to entropy levels of 0.9-0.99. U.S.Pat. No. 6,862,605, issued Mar. 1, 2005, to Wilber, which isincorporated by reference, teaches a random number generator comprisingsoftware that is operable utilizing only elements usually contained in ageneral purpose computer, and a general method for calculating entropyusing the amount of jitter in various oscillatory signals.

FIG. 16 depicts schematically a random number generator system 750comprising a plurality of independent ring oscillator random numbergenerators 752, all clocked by a common clock source F3 (not shown). Therandom bits 754 from each of independent generators 752 are combined inan Exclusive-OR (XOR) gate. The output 758 of the XOR gate containsentropy from all of the plurality of XOR input bits 754. The number ofindependent generators 752 combined this way is increased until thefinal output bits 758 contain the desired entropy.

In some embodiments, a converter is operable to perform at least onetruth table function. Generally, a truth table function in accordancewith the invention is operable to accept an input comprising asubsequence of non-deterministic random binary bits having f number ofbits and to generate an output having g number of bits, wherein f≧2 and0≦g<f. Generally, a truth table function is operable to accept inputbits having an input bit rate, and to generate output bits having anoutput bit rate less than or equal to the input bit rate.

In some embodiments, truth table functions are performed using logicgates. For example, truth table functions for autocorrelation andcross-correlation converters are implemented by one or more two-inputexclusive-NOR (XNOR) gates. In some embodiments, truth table functionsare performed using software algorithms.

In some embodiments in accordance with the invention, a convertercomprises a plurality of truth table functions. Some embodimentscomprise one or more integrated circuit operable to perform one or aplurality of truth table functions. Examples of suitable integratedcircuits include discrete logic chips, CPLDs, integrated logic circuits,and ASICs. In some embodiments, the converter comprises afield-programmable logic array programmed to perform one or a pluralityof truth table functions. Some embodiments comprise a plurality ofsubstantially identical truth table functions.

Generally, a truth table function is operable to accept an inputcomprising a subsequence of non-deterministic random binary bits havings number of bits and for generating an output having t number of bits,wherein s≧2 and 0≦t<s. When present, one or more subsequent truth tablefunctions are operable to accept an input comprising u number of outputbits from a prior truth table function and generating an output having vnumber of bits, wherein u≧2 and 0≦v<u. In some embodiments, t<s/2. Insome embodiments, v<u/2. In some embodiments, a truth table function isoperable such that, on average, t is about s/4. in some embodiments, asubsequent truth table function is operable such that, on average, v isabout u/4. In some embodiments, a converter comprises not less than fivetruth table functions located in series. In some embodiments, theconverter comprises not less than 10 truth table functions located inseries.

In some embodiments, a truth table function is operable to receive aninput comprising two simultaneous non-deterministic random binary bitsto produce an output obeying the following Truth Table, in which xdenotes no output:

input 1 input 2 output 0 0 0 0 1 x 1 0 x 1 1 1

Similarly, in some embodiments, a truth table function is operable toreceive an input comprising two sequential random binary bits to producean output according to the same Truth Table.

For example, FIG. 17 contains a block diagram of a preferred truth tablecircuit 800 that is operable to receive an input comprising twosequential random binary bits to produce an output obeying the truthtable shown above. Such a circuit functions as a bias amplifier. Latches804, 805 are operable to store consecutive input bits 801, 802,respectively. Divide-by-two counter latch 810 is operable to divideclock frequency input by two. Logic AND gate 814 looks at twoconsecutive bits 801, 802 and produces an output “1” if both input bitshave value “1”, and output “0” if both input bits have value “0”. LogicXOR gate 816 combines with logic NOR gate 818 to produce a clock outputif input bits 801 and 802 are the same.

The following equations relate the average output bit rate and bias tothe average input bit rate and input bias, with bias, B, expressed asthe fraction of ones to total bits with B ranging from 0 to 1 and 0.5being the unbiased expectation.

$\begin{matrix}{B_{out} = \frac{{.5}B_{in}^{2}}{{.5} - B_{in} + B_{in}^{2}}} & \left. 1 \right) \\{\overset{\_}{{br}_{out}} = {\overset{\_}{{br}_{in}}\left( {{.5} - B_{in} + B_{in}^{2}} \right)}} & \left. 2 \right)\end{matrix}$

FIG. 18 contains a block diagram of a preferred truth table circuit 830that is operable to receive an input comprising two simultaneous(synchronous) random binary bits 831, 832 to produce an output obeyingthe truth table shown above. This is a mutual bias converter. A circuit830 that is operable to receive two simultaneous input bits isparticularly useful to perform a truth table function of a converterthat receives its input directly from two independent RNG output linesbecause only this data is certain to be simultaneous (or synchronous).

In some embodiments in accordance with the invention, a truth tablefunction is operable to receive an input comprising two sequentialoutput bits from a prior truth table function to produce an outputobeying the same Truth Table:

input 1 input 2 output 0 0 0 0 1 x 1 0 x 1 1 1

Thus, a circuit 800 as depicted in FIG. 17 is also useful to processoutput from a preceding truth table function.

In some embodiments in accordance with the invention, a converterfurther comprises a converter truth table function, the converter truthtable function being operable to convert a plurality of inputsubsequences of random binary bits having a first pattern tosubsequences of random binary bits having a second pattern. In someembodiments, a converter truth table function is operable to receive aninput comprising two random binary bits from a source and to produce aconverter output obeying the following Truth Table:

input 1 input 2 output 0 0 1 0 1 0 1 0 0 1 1 1

If the input bits are from the same sequence, then the converter is anautocorrelation converter. If the input bits are from two independentsimultaneous sequences, then the converter is a cross-correlationconverter. This truth table function is implemented in hardware by atwo-input exclusive-NOR logic gate.

A runs converter compares consecutive bits in a sequence of bits. A runsconverter typically uses an overlapping window of input bits that slidesone bit along the input sequence to produce a conversion for each newinput bit. Converters for runs longer than one use a window of inputbits of length equal to the run length to be converted plus two bits.For each runs converter for runs of length N, the corresponding truthtable will have 2 to the power of N+2 possible input patterns and onlytwo active outputs. The active outputs correspond to the two patternswhere the first and last input bits are equal and all other input bitsare the complement of the first and last input bits. The active outputis equal to the complement of the first or last input bit. The runsconverter produces a no-output condition, x, for all other patterns.

A simple, two-run converter essentially looks at four bits at a time: afirst bit, a last bit and two middle bits. When the two middle bits areidentical, and the first and last bits are both different from the twomiddle bits, then there is a run of two bits. In this case, if bothmiddle bits have a value of “1”, then there is an output of “1”. If bothmiddle bits have a value of “0”, then there is an output of “0”. If onthe other hand, the middle bits are not identical, then there is nooutput. Similarly, a runs-of-three converter looks at five bits, a firstbit, a last bit and three middle bits. A runs-of-one converter usesthree consecutive bits in a sequence.

Typically, the operation of a runs converter is represented as a truthtable function. A truth table for a runs-of-one converter is shown here:

input 1 input 2 intput3 output 0 0 0 x 0 0 1 x 0 1 0 1 0 1 1 x 1 0 0 x 10 1 0 1 1 0 x 1 1 1 x

A runs converter is useful in several ways. A runs converter detects aproperty of a sequence of bits that is responsive to an influence ofmind. Also, a runs-of-two converter detects a response to mentalinfluence that is independent of the results of a bias amplifier or ofan autocorrelation converter. Therefore, a runs converter is useful toindependently assess the reliability of the results of a detection of aninfluence of mind.

The fraction of runs with a given value, for example, “1”, of a givenrun-length, versus the total number of bits in a sequence of random bitsthat are un-influenced by mind, produces a known distribution. Thisdistribution is given by the equation: Number of Runs/Total Bits inSequence=0.25/2^(RL), where RL is the Length of Runs in bits. When asequence of random bits is influenced by mind, the influence of mindchanges the distribution of runs having a given length and a givenvalue. Generally, when a given type of bit (e.g., “1”) corresponds withan intended output, the fraction of runs of the given type in runs ofshorter length decreases with influence of mind, and the fraction ofruns of the bit type in runs of longer length increases. FIG. 19contains a logarithmic graph in which the fraction of runs in a sequenceof bits of a given type (“1” and “0”) is plotted as a function of lengthof runs (in bits). The diagonal curve 1 represents the average fractionof runs of bits both of type “1” and type “0” occurring in a sequence ofrandom bits that is un-influenced by mind. Curve 2 in the graph of FIG.19 represents the fraction of runs of “1” bits of various run-lengthswhen “1” bits correspond to an intended result. Curve 3 represents thecorresponding fraction of runs of “0” bits of various run-lengths when“1” bits correspond to the intended result. As shown by curve 2 depictedin the graph, the fraction of runs of “1” bits in short runs of 1decreases as a result of an influence of mind, while the fraction ofruns of “1” bits in longer runs of 2 or more increases as a result of aninfluence of mind. Conversely, curve 3 shows that the fraction of runsof “0” bits (which correspond to “misses”, i.e., results counter tointended results) increase for short runs of 1 and decrease for longerruns of 2 or more. In some cases, when the influence of mind on therandom number sequence is stronger, the fraction of runs of “1” bitsdecreases in runs of 1 and 2, and increases in longer runs of 3 orlonger, with a corresponding inverted result for runs of “0” bits.

A transitions converter compares consecutive bits in a sequence andgenerates an output when there is a transition from one bit value toanother. When there is no transition, there is no output. Typically, theoperation of a transitions converter can be represented as a truth tablefunction.

A truth table for a two-bit transition converter is shown here:

input 1 input 2 output 0 0 x 0 1 1 1 0 0 1 1 x

A two-bit converter only uses non-overlapping windows of input bits.Transition converters longer than two bits can use either overlapping ornon-overlapping windows of input bits.

A truth table for a three-bit transition converter is shown here:

input 1 input 2 intput3 output 0 0 0 x 0 0 1 1 0 1 0 x 0 1 1 x 1 0 0 x 10 1 x 1 1 0 0 1 1 1 x

In some embodiments, a converter truth table function is operable toaccept a subsequence of random bits from a sequence of random binarybits and to convert a statistical autocorrelation in the subsequence toa bias. A circuit corresponding to circuit 850 depicted in FIG. 20having a single storage latch 852 is suitable for converting first orderautocorrelation to bias. Higher order autocorrelation is converted tobias by adding additional latches in series. For example, a circuitsimilar to circuit 850 in FIG. 20 but including a second latch in serieswith latch 852 is suitable for converting second order autocorrelationto bias.

In some embodiments, the converter comprises a bias amplifier, and aconverter truth table function is operable to accept sequences ofsimultaneous random binary bits and to convert a statisticalcross-correlation in the sequences to a bias. For example, a circuitcorresponding to circuit 850 depicted in FIG. 20 but having no latch,that is, a two-input Exclusive-Nor (XNOR) logic gate, is suitable forconverting cross-correlation to bias.

In some embodiments of an anomalous effect detector, the convertercomprises a plurality of initial truth table functions. Each initialtruth table function forms a path for processing random binary bits, thepath being parallel to other the initial truth table functions. Also,each initial truth table function is operable to accept an inputcomprising a subsequence of random binary bits having s number of bitsand to generate an output having t number of bits, wherein s≧2 and0≦t<s. Some embodiments further comprise one or more subsequent truthtable functions located consecutively in each of one or more of theparallel paths. A subsequent truth table function is operable to acceptan input comprising u number of output bits from a prior truth tablefunction and generating an output having v number of bits, wherein u≧2and 0≦v<u. In some embodiments, the converter comprises not less thanfive subsequent truth table functions in a path. In some embodiments,the converter comprises not less than 10 subsequent truth tablefunctions in a path. In some embodiments, t≦s/2. In some embodiments,v≦u/2. In some embodiments, an initial truth table function is operablesuch that, on average, t is about s/4. In some embodiments, a subsequenttruth table function is operable such that, on average, v is about u/4.

FIG. 21 contains a block diagram representing a converter 870 inaccordance with the invention that is operable to accept subsequences ofrandom binary bits from six sequences of randomness corrected data 871,872, 873, 874, 875, 876 of a random number source. Converter 870contains three parallel input truth table functions 880, 882, 884. Inputtruth table function 880 accepts data from corrected random numbersequences 871, 872. Similarly, input truth table functions 882, 884accept data from sequences 873, 874 and 875, 876, respectively. Theoutput of input truth table functions 880, 882, 884 is accepted bysubsequent truth table functions 890, 892, 894, respectively. Typically,as depicted in FIG. 21, each of the three parallel data processing pathsinitiated by input truth table functions 880, 882, 884 include aplurality of subsequent truth table functions 890, 892, 894,respectively. As a result of the repeated application of truth tablefunctions, data in a given parallel path is decreased. For example, insome embodiments, converters used to measure bias in input data andhaving a total of 15 consecutive truth table functions in a path (e.g.,the path including truth table functions 880, 890 in FIG. 21) accomplisha decrease in data by a factor of about 2³⁰. Truth table functions inaccordance with the invention typically have a high statisticalefficiency. As a result, anomalous effect detectors in accordance withthe invention typically achieve very high statistical efficiency; forexample, virtually 100 percent.

Converter 870 was described with reference to FIG. 21 above to acceptsix sequences of randomness corrected random numbers. It is understoodthat a variation of converter 870 is also operable to accept a singlesequence of randomness corrected random numbers that is split intosubsequences that are then processed in parallel paths.

In some embodiments in accordance with the invention, a converter isoperable to have a theoretical information rate of at least 0.5 bits perminute.

Generally, an anomalous effect detector or a quantum computer inaccordance with the invention is operable such that the theoreticalinformation rate, R, may be calculated using the equation: R=bit rate(1+p Log₂ p+(1−p)Log₂ (1−p)).

Information Rate, R, is: R=bit rate (1−H) where bit rate is the numberof bits/second, or other unit of time, and H is the mathematical entropyof the signal.

Shannon entropy for binary bit streams is defined: H=−p Log₂ p−(1−p)Log₂ (1−p), where p=number of correct bits (“Hits”)/total number ofbits. R is the theoretical noise-free information transfer rate inbits/unit time. Information rate R is broadly usable in accordance withthe invention to quantify both the capabilities of operators and theresponsiveness or sensitivity of devices in the field of anomalouscognition, and is superior to other measures previously used for thispurpose.

Generally, a determined quantum value is obtained by the act ofmeasuring it. In embodiments in accordance with the invention, anon-deterministic random bit is generated by measuring it. It can beviewed that before measurement, there is a superposition of “1” and “0”.For example, a measurement occurs when a LF oscillator latches a HFcounter. In such a case, a bit does not have a value of “1” or “0” untilit is latched. The indeterminacy of timing of the LF oscillator (orother random noise source) makes the measured bit non-deterministic(i.e., true random rather than pseudorandom). The indeterminacy oftiming is caused by shot noise or thermal noise or both, which are atleast partially quantum mechanical.

Studies have shown and it is now widely recognized that a human mind(and presumably the mind of other sentient beings) is capable ofinfluencing quantum probabilities. See, for example: Quantum physics inneuroscience and psychology: a neurophysical model of mind-braininteraction, by Jeffrey M. Schwartz et al., Phil. Trans. R. Soc. B, TheRoyal Society (2005) (published online); Quantum Collapse, Consciousnessand Superluminal Communication, by Gao Shan, Chinese Institute ofElectronics (published online); Visual Conscious Experience, by MitjaPerus, BION Institute,Ljubljana, Slovenia (published online). Anembodiment in accordance with the present invention is operable torespond to an influence of mind of a human operator (or other sentientorganism) on quantum mechanical wavefunctions. It is believed that amongother effects, mind influences quantum mechanical wavefunctions to causeentanglement. Further, it is believed that mind causes quantummechanical entanglement of truly random bits in a physical source 404,434 of entropy with target information. An embodiment of quantumcomputer 400, 430 is particularly useful when desired target informationis hidden or non-inferable from currently available information. Mindinfluences sources of entropy, such as sources 404, 434, which arequantum mechanical. The influence of mind shifts a probability orprobabilities of the results of measurements of bits in entropy sources404, 434. Such shifts of probabilities of bits include shifts in bias,autocorrelation, and other more complex probabilities and properties. Inaccordance with the invention, measurement processor 410, 440 enhancesthe effects of shifted probabilities of bits in the stream of numbersfrom source 404, 434.

FIG. 22 depicts schematically quantum computer 900 in accordance withthe invention. Quantum computer 900 comprises physical source of entropy910 that is operable to generate a plurality of output numbers 912. Insome embodiments in which entropy source 910 comprises a LF oscillatorand a HF counter, the entropy in a word read from the counter isestimated by analyzing a sequence of words read from the counter.Exemplary amounts of entropy are in a range of from 1.0 to 3.0 bits. Insome embodiments which have a 1 kHz LF oscillator and in which theentropy in a counter is greater than 1.0 and less than 2.0, entropysource 910 generates output bits, each having an entropy approaching1.0, at a rate of 1000 bits per second. Each output bit that isgenerated is directed to one of the plurality of output streams 912. Theoutput bits are generated, however, in rapid succession by entropysource 910. Each of output bit streams 912 is processed in accordancewith the invention in converter 920.

Processor 924 accepts converter output bits 922. Quantum computer 900also comprises source 926 of test bits 927. Processor 924 processes theplurality of converter output bits 922, including measuring whetherthere is a match between processor results and a test bit. Processor 924then generates a combined processor output 928, which is sent to resultsinterface 930. Processor output bit 928 is representative of aninfluence of mind. Processor output bit 928 contains fewer numbers thanthe input of converter output bits 922.

It is believed that an operator's mind influences quantum probabilitiesin entropy source 910 and in test number source 926, resulting in atleast partial entanglement of the quantum mechanical entropy sources ofentropy source 910 and test number source 926, thereby increasing theprobability of a match between the measurement of the influence of mindand a test number.

FIG. 23 depicts schematically a quantum computer 940 in accordance withthe invention. Quantum computer 940 comprises a plurality of entropysources 950 that are operable to generate a plurality of output streams952. Quantum computer 940 further includes a plurality of converters 960in accordance with the invention, corresponding to each of entropysources 950. Each output stream 952 is processed in accordance with theinvention in a converter 960. Processor 964 accepts converter output962. Processor 964 processes converter output 962 in accordance with theinvention. Processor 964 then generates processor result 966, which issent to results interface 968.

It is believed that the entropy sources in the plurality of entropysources 950 are entangled in a quantum mechanical sense. According toone view, actual sources 950 are entangled, not necessarily the numbers(bits). According to this view, the numbers that come out of entropysources 950 have a relationship because entropy sources 950 wereentangled. Also, random number sources 950 are entangled not only witheach other but with the intended outcome, hidden or non-inferableinformation. It is believed that an operator's mind influences quantumprobabilities in entropy sources 950, resulting in at least partialentanglement of the quantum mechanical entropy sources of entropysources 950 and target information (e.g., a test bit or desired unknowninformation), thereby increasing the probability of a match between theresult 966 and target information.

According to one view, actual bits are not entangled. Once measured (orlatched), the bits are determined. According to this view, if a bit is 1or 0, it cannot be entangled. According to this view, entropy sourcesare entangled. After a quantum computer in accordance with the inventiongenerates a random bit, that is, after measurement is made,superposition exists no longer. But, since a source can be entangled,for example, with a future bit or with unknown information, and sincesources can be entangled, one or more physical sources of entropy inaccordance with the invention generate one or more streams of outputbits having a coherent pattern, from which the processor produces thedesired result. Multiple entangled sources give a greater effect than asingle source, which is entangled only with the intended result. Atraditional quantum computer typically is run several times to obtain areliable result because a quantum computer's output is alwaysprobabilistic.

A quantum computer in accordance with the invention responds toinfluences of the mind of an operator on quantum probabilities ofphysical sources of entropy or quantum circuits. Accordingly, mindinfluences sources of entropy, such as sources 404, 434, 910, 950, whichare at least partially quantum mechanical. The influence of mind shiftsa probability or probabilities in a source of entropy. It is understoodthat an operator need not be spatially close to a quantum computer inaccordance with the invention to achieve good results. In fact, in someembodiments, an operator is located hundreds and even thousands of milesaway from the one or more sources of entropy 404, 434, 910, 950.Similarly, in embodiments in accordance with the invention comprising asource of test numbers 406, 436, 926, the operator need not be in closespatial proximity to the source. Also, in some embodiments, the operatordoes not physically initiate operation of the quantum computer (such asby using a keypad).

A mental influence detector in accordance with the invention is usefulin a wide variety of applications. Some embodiments are operable as aninformation accuracy enhancement device, particularly when theinformation is not accessible by classical methods. Some embodiments areoperable as a predicted information accuracy enhancement device,particularly when the predicted information is not accessible byclassical methods. Some embodiments are operable as a communicationaccuracy enhancement device, particularly when the communication is notaccessible by classical methods. Some embodiments are operable torespond directly to an influence of mind. Some embodiments are operableto respond to mental intention in conjunction with a game.

In some embodiments in accordance with the invention, a mental influencedetector is portable. Some embodiments further comprise a connection tothe internet. In some embodiments, a mental influence detectorinterfaces to a device, such as a computer or server, that is connectedto the internet. Functions of a mental influence detector having highprocessing rates (e.g., on the order of billions of operations persecond) are typically in an FPGA or other hardware implementationbecause of the very high processing rates (too fast to process by acomputer). Nevertheless, additional truth table processing may be donein a computer before the information is sent over the internet or overphone or a wireless connection to an output device where an operator islocated. Any connection to other physiological measurement devicestypically is done where the operator is located, but the results may besent back to a “base” location where the detector is located for furtherprocessing and correlation. In some embodiments, the results of thisfurther processing is then sent back to the output device for use by theoperator. In other words, in embodiments in accordance with theinvention that are internet-based, the several components of ananomalous effect detector or of a quantum computer in accordance withthe invention are located separately from each other, sometimesseparated by hundreds or thousands of miles.

In some embodiments, the mental influence detector is operable in acombination with a game, and the combination includes a source of asequence of random binary bits.

Some embodiments in which the mental influence detector is operable inthe combination with the game further comprise a connection to theinternet.

FIG. 24 contains a block diagram of an internet-based system 970 inaccordance with the invention. System 970 includes at least one mentalinfluence detector, but several of the components of a mental influencedetector are spatially separated from each other, possibly by largedistances up to hundreds or thousands of miles. System 970 comprises abase module 971, which base module includes a source of numbers (NDRN),a converter and a processor in accordance with the invention. Basemodule 971 is connected to a conventional internet server 972. Through aconventional internet connection 973, an internet service provider 974and a user internet connection 975, a user's personal computer (PC) 976or other internet-enabled device is connected to base module 971.Typically, user PC 976 includes an output device, which is utilized as aresults interface 977 in accordance with the invention to convey resultsto an operator 978. Typically, system 970 comprises an initiator 979connected to user PC 976, with which operator 978 initiates detection bysystem 970 of an influence of mind. Some embodiments include test numbersource 980 in accordance with the invention. Preferably, test numbersource 980 is located in or proximate to user PC 976.

Certain common features have been observed during hundreds of testingsessions with some embodiments of mental influence detectors inaccordance with the invention. In certain applications of a mentaleffect detector, a lack of attention, drifting or loss of focustypically correspond to a reduction in results. A new operator, or anexperienced operator, when significant changes have been made tohardware configuration or processing methods, usually benefits from aninitial learning period. After a learning period has passed, resultstypically follow a pattern of rapid initial rise in the measuredinfluence of mind followed by a peak shortly after the beginning of thesession, and then a gradual decline down to some positive basal levelsubstantially lower than the peak. Experienced operators typicallymaintain the basal level longer and at a higher level than neweroperators. Persistence, regularity and motivation to train usually boostthe abilities of an operator. These abilities are cumulative over thelong term and show persistence in other areas of the operator's life.Certain conditions of the operator may reduce measured mental influence.These include physical discomfort or illness, mental upset ordistraction and physical or mental fatigue.

Embodiments of mental influence detectors in accordance with theinvention are applicable in various areas of anomalous cognition andmachine-enhanced anomalous cognition. These include areas of researchinto mind, consciousness and reality that are variously referred to asESP, Psi, Psychic Phenomena, Remote Viewing, Telepathy, Clairvoyance,Clairaudience, Psychokinesis, Precognition, Mental Powers, among other.Some specific areas of utilization include communications, enhanceddecision making, medical diagnosis and treatment options, enhancedcomputing machines, lie detection, enabling the handicapped, locatinglost or hidden objects, and increasing correct prediction probabilitiesfor everything from games of “chance” to market moves.

The particular systems, devices and methods described herein areintended to illustrate the functionality and versatility of theinvention, but should not be construed to be limited to those particularembodiments. It is evident that those skilled in the art may now makenumerous uses and modifications of the specific embodiments described,without departing from the inventive concepts. It is also evident thatthe steps recited may, in some instances, be performed in a differentorder; or equivalent structures and processes may be substituted for thestructures and processes described. Since certain changes may be made inthe above systems and methods without departing from the scope of theinvention, it is intended that all subject matter contained in the abovedescription or shown in the accompanying drawings be interpreted asillustrative and not in a limiting sense. Consequently, the invention isto be construed as embracing each and every novel feature and novelcombination of features present in or inherently possessed by thesystems, devices and methods described in the claims below and by theirequivalents.

1-166. (canceled)
 167. A method of detecting an influence of mind,comprising: providing input numbers from a source of non-deterministicrandom numbers; converting a property of said input numbers into aconverter output representative of said property; producing a processoroutput signal representative of an influence of mind by processing saidconverter output, wherein said processor output signal contains fewernumbers than said input numbers; and communicating said processor outputsignal.
 168. A method of detecting an influence of mind as in claim 167wherein said providing said input numbers comprises: reducing astatistical defect in said input numbers.
 169. A method of detecting aninfluence of mind as in claim 168 wherein: said reducing a statisticaldefect reduces bias in said input numbers to less than 10 ppm andreduces autocorrelation of any order in said input numbers to less than10 ppm.
 170. A method of detecting an influence of mind as in claim 167wherein said providing input numbers comprises: generating input numbershaving a bias less than 10 ppm and an autocorrelation less than 10 ppmfor any order.
 171. A method of detecting an influence of mind as inclaim 167 wherein: said converting a property of said input numberscomprises at least one step of converting selected from the groupconsisting of: converting a cross-correlation to a bias, converting amutual bias to a bias, and converting runs to a bias.
 172. A method ofdetecting an influence of mind as in claim 167 wherein said converting aproperty of said input numbers comprises: amplifying a bias of saidinput numbers.
 173. A method of detecting an influence of mind as inclaim 172 wherein said amplifying a bias comprises: performing a truthtable bias function.
 174. A method of detecting an influence of mind asin claim 172 wherein said amplifying a bias comprises: performing abounded random walk.
 175. A method of detecting an influence of mind asin claim 167 wherein said converting a property of said input numberscomprises: converting a cross-correlation between a plurality ofsimultaneously generated input numbers into a bias contained in across-correlation converter output.
 176. A method of detecting aninfluence of mind as in claim 167 wherein said converting a property ofsaid input numbers comprises: converting autocorrelation in said inputnumbers into a bias contained in an autocorrelation converter output.177. A method of detecting an influence of mind as in claim 167, furthercomprising: providing at least one test number to said processor;measuring a relationship between said converter output in said processorand said at least one test number to produce a relationship measurement;and in said processor, abstracting said relationship measurement toprovide an enhanced output signal representative of said influence ofmind.
 178. A method of detecting an influence of mind as in claim 177wherein said providing said at least one test number comprises:providing test numbers having a fixed pattern.
 179. A method ofdetecting an influence of mind as in claim 177, further comprising:initiating a detection of said influence of mind using an initiator; andwherein said providing said at least one test number comprises providingat least one test number before said initiating said detection.
 180. Amethod of detecting an influence of mind as in claim 177, furthercomprising: initiating a detection of said influence of mind using aninitiator; wherein said providing said at least one test numbercomprises providing at least one test number after said converting aproperty of said input numbers into a converter output.
 181. A method ofdetecting an influence of mind as in claim 167 wherein said providingsaid input numbers comprises: providing input numbers from a source ofnon-deterministic random numbers located in an integrated circuit. 182.A method of detecting an influence of mind as in claim 181 wherein saidproviding input numbers comprises: using an independent ring oscillator.183. A method of detecting an influence of mind as in claim 167, furthercomprising: initiating a detection of said influence of mind.
 184. Amethod of detecting an influence of mind as in claim 183 comprising:receiving a conditioned physiological measurement to initiate adetection.
 185. A method of detecting an influence of mind as in claim183 comprising: receiving an output from an influence-of-mind detectorto initiate a detection.
 186. A method of detecting an influence of mindas in claim 183 wherein said detection is initiated automatically andperiodically.
 187. An influence-of-mind detector, comprising: a sourceof non-deterministic random numbers; a converter operable to acceptinput numbers from said source and to convert a property of said inputnumbers into a converter output containing bias representative of saidproperty; a processor that is operable to accept said converter outputand to produce a processor output signal representative of an influenceof mind; wherein said processor output signal contains fewer numbersthan said input numbers; and an interface that is operable tocommunicate said processor output signal.
 188. An influence-of-minddetector as in claim 187 wherein said source of non-deterministic randomnumbers comprises: a randomness corrector.
 189. An influence-of-minddetector as in claim 187 wherein said converter comprises: a biasamplifier that is operable to amplify bias of said input numbers. 190.An influence-of-mind detector as in claim 187 wherein said convertercomprises: an autocorrelation converter that is operable to convertautocorrelation in said input numbers into a bias that is contained inan autocorrelation converter output.
 191. An influence-of-mind detectoras in claim 187, further comprising: a source of test numbers; andwherein said processor is operable to measure a relationship betweensaid converter output in said processor and at least one test number toproduce a relationship measurement; and said processor is furtheroperable to abstract said relationship measurement to provide anenhanced output signal representative of said influence of mind.
 192. Aninfluence-of-mind detector as in claim 187 wherein said source ofnon-deterministic random numbers is located in an integrated circuit.193. An influence-of-mind detector as in claim 192 wherein said sourceof non-deterministic random numbers comprises: an independent ringoscillator.
 194. An influence-of-mind detector as in claim 187, furthercomprising; an initiator that is operable to initiate a detection of aninfluence of mind.
 195. A method of using a quantum computer that isresponsive to an influence of mind, comprising: generating outputnumbers using physical sources of entropy, which sources are at leastpartially entangled; processing a relationship between said outputnumbers to produce a result representative of an influence of mind; andcommunicating said result.
 196. A quantum computer for responding to aninfluence of mind, comprising: a plurality of physical sources ofentropy operable to generate output numbers, said physical sources beingat least partially entangled; a processor, said processor being operableto process a relationship between said output numbers to produce aresult representative of an influence of mind; and an interface that isoperable to communicate said result.